Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 2, 9-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim(s) 2 “recites” abstract ideas under the 2019 PEG: Mathematical concepts (mathematical relationships and calculations, including training and inference of machine-learned models): “predicting an identity of a driver … using a machine-learned facial recognition model;” “updating training of the machine-learned facial recognition model…”
These are mathematical modeling and optimization operations. See SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1167–70 (Fed. Cir. 2018).
Mental processes (observations and evaluations practically performable in the human mind) per the Oct. 2019 Update:
“predicting an identity of a driver based on the image” (identity recognition is an evaluative judgment at a high level); “based on a confirmation or correction of the predicted identity” (human evaluation/feedback).
Data gathering/presentation (insignificant extra-solution activity):
“receiving an image of a face captured during a safety event…” (data acquisition).
The “safety event inside a vehicle” context is an intended use/field-of-use limitation, not a technological improvement.
Claims 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim(s) 9 is directed to the abstract idea of comparing new and stored information and using rules to identify options. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements do not add meaningful limitations to practicing the abstract idea.
Claims 9 recite, in part, a method of receiving an image, predicting an identity of a driver and updating the model based on a confirmation/correction. These steps correspond to concepts identified as abstract ideas by the courts, such as collecting and comparing known information, comparing data to determine a risk level, and comparing new and stored information and using rules to identify options. The concept described in claims 9-15 are not meaningfully different than the abstract ideas determined by the courts. As such, the concepts of claims 9-15 are an abstract idea.
Additionally, the claims do not add additional elements that are sufficiently to amount to significantly more than the judicial exception because the additional elements, when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
Therefore, claims 9-15 are not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 2-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nemat-Nasser et al (US 2014/0324281) in view of Taigman et al (US 2015/0125049).
Per claim 2, 9 and 16, Nasser teaches a system comprising: one or more computer processors; one or more computer memories; and a set of instructions incorporated into the one or more computer processors, the set of instructions configuring the one or more computer processors to perform operations (0017 teaches computer processor…data storage and set of instruction incorporated into one or more processors to perform actions), the operations comprising:
receiving an image of a face captured during a safety event inside a vehicle (0016, 0031-0032, teaches image capturing device is positioned to capture an image of a driver…image capture may occur upon a triggering event…plurality of images are captured around or at the time of the driving data collection and driving data may include driving event data);
predicting an identity of a driver based on the image using [a machine-learned] facial recognition model (abstract, 0015-0016 teaches processor is configured to detect a face in the image…determine a set of face data from the image…the driver is identified based on the face data and face data can be used to identify the driver by employing a model of faces); and
updating training of the [machine-learned] facial recognition model based on a confirmation or correction of the predicted identity of the driver (0032 and 0028 teaches training model based on confirmation of identification. Further, Nasser teaches the model output is trained using face data and corresponding human confirmation of correct identification and a supervisor confirms the identity of a driver by viewing a still image or video clip uploaded from the vehicle).
But, Nasser does not explicitly teach machine-learned facial recognition model and updating training of the machine-learned facial recognition model.
However, in an analogous art, Taigman teaches a system/method for facial representation (abstract). Taigman further teaches machine-learned facial recognition model and updating training of the machine-learned facial recognition model (abstract, 0002, 0082 teaches classify face images by employing a deep neural network. The present technology provides techniques for representing facial images using deep learning by providing to a DNN. Each filter may be configured to extract a feature of an image and the feature vector includes various features of the image and be used to represent the image. 0069 and 0080 teaches training a DNN with datasets, and datasets may include a large collection of photos and some of the photos may be identified/labeled/tagged. The loss may be minimized by updating the parameters using stochastic gradient descent). Therefore, before the effective filling date of the invention, it would have been obvious to one of ordinary skill to implement the driver identification system of Nasser using the deep-learning facial recognition model of Taigman to improve the accuracy of driver identification during driving events, because both references are directed to identifying individuals from facial images.
Per claim 3, 10 and 17, Nasser teaches wherein predicting of the identity of the driver comprises processing the image to detect facial features of the face (0016 teaches the image is analyzed to locate a face in the image and extract facial features).
Per claim 4, 11 and 18, Nasser teaches wherein the predicting of the identity of the driver comprises comparing the detected facial features to stored facial features associated with known drivers (0016 teaches determine facial feature and the face data may be compared to face data stored in the database, the driver may be identified based on the comparison).
Per claim 5, 12 and 19, Nasser teaches wherein the predicting of the identity of the driver comprises determining a correspondence between the detected facial features and the stored facial features that transgresses a threshold level of correspondence (0026 the face data obtained from the image may be compared with face data stored in a database, and if a similarity score exceeds a predefine threshold value, the driver may be identified).
Per claim 6, 13 and 20, Nasser teaches wherein the updating of the training comprises receiving an input from an administrator, the input accepting or rejecting the predicted identity of the driver (0028 teaches a supervisor is capable of confirming/denying identify of the driver based on the image).
Per claim 7, 14 and 21, Nasser in view of Taigman teaches wherein the updating of the training comprises using the input as training data to improve future matches between faces capture in images and known drivers (as shown in rejection of claim 1-6, Nasser teaches manual assignment by the human operator/supervisor. Taigman in 0002, 0082 teaches classify face images by employing a deep neural network. The present technology provides techniques for representing facial images using deep learning by providing to a DNN. Each filter may be configured to extract a feature of an image and the feature vector includes various features of the image and be used to represent the image. 0069 and 0080 teaches training a DNN with datasets, and datasets may include a large collection of photos and some of the photos may be identified/labeled/tagged. The loss may be minimized by updating the parameters using stochastic gradient descent). Therefore, before the effective filling date of the invention, it would have been obvious to one of ordinary skill in the art to use administrator confirmed of Nasser as labeled training data to retrain the facial recognition model of Taigman, because supervised facial recognition systems routinely improve future matching accuracy by incorporating corrected labels into training datasets.
Per claim 8 and 15, Nasser in view of Taigman teaches wherein predicting of the identity of the driver comprises: receiving a series of images capturing the face of the driver from different viewing angles during operation of the vehicle; adding one or more of the series of images to a club of images associated with the driver, wherein each image in the club has a threshold degree of similarity to other images in the club; and using the club of images to train the machine-learned facial recognition model (Nasser as mentioned in rejection above, teaches capturing image of a driver, a plurality of images are captured around or at the time of driving data collection. Taigman teaches in 0025-0028 datasets may include large collection of photos and datasets may include multiple images of the same individual from different angles/distance. Taigman further teaches image store comprises a set of face images corresponding to an individual. The feature victor includes various features of the image and the identify may be determined based on similarity between feature vectors. Taigman further teaches classification may be performed by applying a threshold to the similarity measure. 0069 and 0080 teaches training a DNN with datasets, and datasets may include a large collection of photos and some of the photos may be identified/labeled/tagged. The loss may be minimized by updating the parameters using stochastic gradient descent, so the set of images (club) associated with an individual is used as training data to train the machine learned facial recognition model). Therefore, before the effective filling date of the invention, it would have been obvious to one of ordinary skill in the art to use multiple images of a driver captured during operation as taught by Nasser as part of the multi-image training datasets taught by Taigman, because facial recognition systems commonly improve robustness by training on multiple images of the same individual that meet similarity thresholds. Using such grouped images to train the model yields predictable improvements in recognition accuracy.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gleeson-May et al (US 2018/0012092), paragraph 0101
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/OMEED ALIZADA/Primary Examiner, Art Unit 2686